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An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process
An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process
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An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process
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An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process
An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process

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An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process
An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process
Journal Article

An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process

2018
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Overview
The process of criteria prioritization and weighting is an important part of multiple attributes decision making. The most frequently applied multi-attribute decision-making weighting tools include analytical hierarchy process, stepwise weight assessment ratio analysis, factor relationship, and best–worst method. When policies are at the core of decision making, stepwise weight assessment ratio analysis method is the most efficient method for criteria evaluation. It involves two important steps: the first is to prioritize the criteria by consulting experts, while the second is the weighting process. This research seeks to extend stepwise weight assessment ratio analysis to improve the quality of the decision-making process by incorporating the reliability evaluation of experts’ idea into the first step. Such a component is absent from the first step in all other similar models. Thus, an extended version of stepwise weight assessment ratio analysis can be applied for such evaluation. To test the applicability and performance of the proposed method, a numerical example from an earlier study was used. The proposed version can replace the classic version in future studies as an improved method in decision-making area.